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Agentailor

Agentailor is a technical blog by Ali Ibrahim, part of agentailor.com — the hub for developers building AI agents. The blog publishes in-depth, code-first articles on building AI agents across all four paths: building it yourself (with frameworks like LangGraph or the OpenAI Agents SDK), building it with a coding agent (agentic engineering with tools like Claude Code), deploying an open-source agent (such as Goose), and using a managed service. It covers the core agent primitives — models, tools

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-23T20:07:25.135Z. Cached copy · Source on agentailor.com

# Agentailor

> Agentailor is a technical blog by Ali Ibrahim, part of agentailor.com — the hub for developers building AI agents. The blog publishes in-depth, code-first articles on building AI agents across all four paths: building it yourself (with frameworks like LangGraph or the OpenAI Agents SDK), building it with a coding agent (agentic engineering with tools like Claude Code), deploying an open-source agent (such as Goose), and using a managed service. It covers the core agent primitives — models, tools, memory, prompting, and the decision loop — along with the Model Context Protocol (MCP), context engineering, agentic engineering, evaluation criteria for choosing an approach, and production considerations. The target audience is software engineers and AI engineers, from those new to agents to experienced practitioners with specific architectural needs. All articles include working code examples.

Note: All article links point to .md files containing clean Markdown. Use these instead of the HTML pages to save tokens.

## Series

Ongoing case studies where each entry builds on the last, rather than standalone tutorials. Start at the hub for the full arc.

### [Cameron](https://blog.agentailor.com/cameron)

A local-first personal finance agent you own and run yourself — an AI agent you can trust with real money because it never acts without your approval. Built in public, one tagged release per article: each entry adds a capability to the same agent instead of starting over, so you learn agent engineering by watching one real system grow.

Hub: https://blog.agentailor.com/cameron — the canonical page for this series (roadmap, design invariants, and every entry).

Entries, in reading order:

- v1 — [Meet Cameron: a personal finance agent you own and run](https://blog.agentailor.com/posts/meet-cameron.md) (devlog): Cameron is a local-first personal finance agent you own and run yourself. v1 shows how an LLM loop, tools, and an approval gate compose into an agent you can trust with real money.
- v2 — [Testing Tools for AI Agents: catching the bugs your agent can't tell you about](https://blog.agentailor.com/posts/testing-tools-for-ai-agents.md) (devlog): You designed the tool well. Nothing verifies it still does what its description promises. An audit of eight tools found four contract bugs, including one that made a finance agent report $2,812 for a $2,752 bill, and the cheap test layer that catches them.
- v2 — [How to Write Your First AI Agent Evals](https://blog.agentailor.com/posts/first-eval-cases.md) (devlog): Where the first cases come from, which ones earn a place in the suite, why every case that pushes a behavior needs one that bounds it, how to grade without an LLM judge, and what the first honest run surfaced.
- v2.1 — [When Your Eval Script Answers a Question Nobody Asked](https://blog.agentailor.com/posts/simulated-users-for-agent-evals.md) (devlog): A scripted multi-turn eval replays fixed user turns regardless of what the agen
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